Swarm intelligence algorithm inspired by route choice behavior

Hu, Junjie; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jun; Wang, Jian

doi:10.1016/s1672-6529(16)60338-4

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…Urban big data enables a highly granular and longitudinal system, and it can help us understand city system and service better [6][7][8][9][10]. It can be used in many fields such as planning and governing cities, and business.…”

Section: Determining the Location Of Depotsmentioning

confidence: 99%

Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

Liu

Jia

Zhu

2018

Scientific Programming

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Car sharing is a type of car rental service, by which consumers rent cars for short periods of time, often charged by hours. The analysis of urban traffic big data is full of importance and significance to determine locations of depots for car-sharing system. Taxi OD (Origin-Destination) is a typical dataset of urban traffic. The volume of the data is extremely large so that traditional data processing applications do not work well. In this paper, an optimization method to determine the depot locations by clustering taxi OD points with AP (Affinity Propagation) clustering algorithm has been presented. By analyzing the characteristics of AP clustering algorithm, AP clustering has been optimized hierarchically based on administrative region segmentation. Considering sparse similarity matrix of taxi OD points, the input parameters of AP clustering have been adapted. In the case study, we choose the OD pairs information from Beijing’s taxi GPS trajectory data. The number and locations of depots are determined by clustering the OD points based on the optimization AP clustering. We describe experimental results of our approach and compare it with standard K-means method using quantitative and stationarity index. Experiments on the real datasets show that the proposed method for determining car-sharing depots has a superior performance.

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Section: Determining the Location Of Depotsmentioning

confidence: 99%

Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

Liu

Jia

Zhu

2018

Scientific Programming

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“…It can be seen as the process of selecting a solution that meets the target requirements as reasonably as possible from many limited or infinite decisions while overcoming the problem in complex solutions that traditional optimization methods cannot solve. At present, the bionic optimization algorithms are divided into evolutionary algorithm ( Wu and Wu, 2017 ), swarm intelligence algorithm ( Tian et al, 2016 ), simulated annealing algorithm ( Kong et al, 2015 ), tabu search algorithm ( Fu et al, 2018 ), and neural network algorithm. Among them, the evolutionary algorithm is divided into genetic algorithm ( Jayasinghe et al, 2015 ), differential evolution algorithm ( Zhang et al, 2018 ), and immune algorithm ( Aragón et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Weed Density Extraction Based on Few-Shot Learning Through UAV Remote Sensing RGB and Multispectral Images in Ecological Irrigation Area

et al. 2022

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With the development of ecological irrigation area, a higher level of detection and control categories for weeds are currently required. In this article, an improved transfer neural network based on bionic optimization to detect weed density and crop growth is proposed, which used the pre-trained AlexNet network for transfer learning. Because the learning rate of the new addition layer is difficult to tune to the best, the weight and bias learning rate of the newly added fully connected layer is set with particle swarm optimization (PSO) and bat algorithm (BA) to find the optimal combination on the small data set. Data are transported to the convolutional neural network (CNN) by collecting red-green-blue (RGB) and 5-band multispectral images of 3 kinds of weeds and 3 kinds of crops as data sets, through cutting, rotating, and other operations. Finally, 6 kinds of classifications are implemented. At the same time, a self-constructed CNN based on model-agnostic meta-learning (MAML) is proposed in order to realize the learning of neural networks with small sample and high efficiency, and its accuracy is verified in the test set. The neural networks optimized by two bionic optimization algorithms are compared with the self-constructed CNN based on MAML and histogram of oriented gradient + support vector machine (HOG + SVM). The experimental results show that the combination of learning rate through BA is the best, and its accuracy can reach 99.39% for RGB images, 99.53% for multispectral images, and 96.02% for a 6-shot small sample. The purpose of the classification proposed in this article is to calculate the growth of various plants (including weeds and crops) in the farmland. And various plant densities can be accurately calculated through the plant density calculation formula and algorithm proposed in this article, which provides a basis for the application of variable herbicides by experimenting in different farmlands. Finally, an excellent cycle of ecological irrigation district can be promoted.

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“…Swarm intelligence algorithms simulate the group behavior of animals and hunt according to group cooperation [22]. More commonly, this is done by mimicking the natural behavior of flocks of birds, whales, bee colonies, and other group organisms [23]. The classical approach is the particle swarm algorithm (PSO), which simulates the predatory and cooperative behaviors of bird groups and individuals, and performs well in solving complex optimization problems [24].…”

Section: Introductionmentioning

confidence: 99%

An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems

Yang

Xia

et al. 2021

Symmetry

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The transient search algorithm (TSO) is a new physics-based metaheuristic algorithm that simulates the transient behavior of switching circuits, such as inductors and capacitors, but the algorithm suffers from slow convergence and has a poor ability to circumvent local optima when solving high-dimensional complex problems. To address these drawbacks, an improved transient search algorithm (ITSO) is proposed. Three strategies are introduced to the TSO. First, a chaotic opposition learning strategy is used to generate high-quality initial populations; second, an adaptive inertia weighting strategy is used to improve the exploration ability, exploitation ability, and convergence speed; finally, a neighborhood dimensional learning strategy is used to maintain population diversity with each iteration of merit seeking. The Friedman test and Wilcoxon’s rank sum test were also used by comparing the experiments with recently popular algorithms on 18 benchmark test functions of various types. Statistical results, nonparametric sign tests, and convergence curves all indicate that ITSO develops, explores, and converges significantly better than other popular algorithms, and is a promising intelligent optimization algorithm for applications.

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Swarm intelligence algorithm inspired by route choice behavior

Cited by 15 publications

References 30 publications

Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

Weed Density Extraction Based on Few-Shot Learning Through UAV Remote Sensing RGB and Multispectral Images in Ecological Irrigation Area

An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems

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